Face

Once computer vision programs detect a face, they can extract data about your emotions, age, and identity.
See how a face is detected

Anti Face

This face is unrecognizable to several state-of-art face detection algorithms.


Camouflage from face detection.

CV Dazzle explores how fashion can be used as camouflage from face-detection technology, the first step in automated face recognition.

The name is derived from a type of World War I naval camouflage called Dazzle, which used cubist-inspired designs to break apart the visual continuity of a battleship and conceal its orientation and size. Likewise, CV Dazzle uses avant-garde hairstyling and makeup designs to break apart the continuity of a face. Since facial-recognition algorithms rely on the identification and spatial relationship of key facial features, like symmetry and tonal contours, one can block detection by creating an “anti-face”.

From all appearances, deception has always been critical to daily survival—for human and non-human creatures alike—and, judging by its current ubiquity, there is no end in immediate sight

Roy Behrens, Camoupedia

Look Book

CV Dazzle Look Book 2010 - present

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Look N° 5 (a)
  • For New York Times Op-Art
  • Model: Bre Bitz
  • Hair: Pia Vivas
  • Makeup: Giana DeYoung
  • Assistant Creative Direction: Tiam Taheri
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Look N° 5 (b)
  • For New York Times Op-Art
  • Model: Bre Bitz
  • Hair: Pia Vivas
  • Makeup: Giana DeYoung
  • Assistant Creative Direction: Tiam Taheri
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Look N° 5 (c)
  • For New York Times Op-Art
  • Model: Bre Bitz
  • Hair: Pia Vivas
  • Makeup: Giana DeYoung
  • Assistant Creative Direction: Tiam Taheri
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Look N° 4
  • For DIS Magazine (2010)
  • Creative direction by Lauren Boyle and Marco Roso
  • Model: Jude
  • Hair: Pia Vivas
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Look N° 3
  • For DIS Magazine (2010)
  • Creative direction by Lauren Boyle and Marco Roso
  • Model: Jude
  • Hair: Pia Vivas
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Look N° 2
  • For DIS Magazine (2010)
  • Creative direction by Lauren Boyle and Marco Roso
  • Model: Irina
  • Hair: Pia Vivas
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Look N° 1
  • For NYU ITP Thesis Presentation (2010)
  • Hair: Pia Vivas
  • Model: Jen Jaffe

Test Patterns for Stylists

Test pattern to block OpenCV, eblearn, VeriLook, and Apple face detection

Download PDF

Test pattern to block OpenCV, eblearn, VeriLook, and Apple face detection

Download PDF


Style Tips for Reclaiming Privacy

1
Makeup

Avoid enhancers: They amplify key facial features. This makes your face easier to detect. Instead apply makeup that contrasts with your skin tone in unusual tones and directions: light colors on dark skin, dark colors on light skin.

2
Nose Bridge

Partially obscure the nose-bridge area: The region where the nose, eyes, and forehead intersect is a key facial feature. This is especially effective against OpenCV's face detection algorithm.

3
Eyes

Partially obscure one of the ocular regions: The position and darkness of eyes is a key facial feature.


4
Masks

Avoid wearing masks as they are illegal in some cities. Instead of concealing your face, modify the contrast, tonal gradients, and spatial relationship of dark and light areas using hair, makeup, and/or unique fashion accessories.

5
Head

Research from Ranran Feng and Balakrishnan Prabhakaran at University of Texas, shows that obscuring the elliptical shape of a head can also improve your ability to block face detection. Link: Facilitating fashion camouflage art

6
Asymmetry

Facial-recognition algorithms expect symmetry between the left and right sides of the face. By developing an asymmetrical look, you may decrease your probability of being detected.


Collaborations

From DIS Magazine's How to Hide from Machines



Press

Presentations
  • Upcoming in 2014: Future Everything. Manchester. March 2014.
  • Quintessenz. Wien, Austria. 2013.
  • SMART Design. 2013
  • Tabula Rasa: Spoofing, Anti-Spoofing Workshop. Rome. 2012.
  • IxDS. Berlin. 2011
  • New York Times Design Series. 2010.
  • Philly Tech Week. 2012
  • HOPE Hackers Conference. 2010


Contact

CV Dazzle is developed by Adam Harvey, an artist whose work explores the impacts of surveillance technologies. His projects have earned a Core77 design award, a Webby Nomination, a commission grant from Rhizome.org, and received press coverage and interviews with the BBC, The Guardian, Wired, Air Force Times, Scientific American, VICE, and Dazed and Confused. He currently lives in Brooklyn.


OpenCV Visualized

OpenCV Face Detection

OpenCV is one of the most widely used face detectors. This algorithm performs best for frontal face imagery and excels at computational speed. It's ideal for real-time face detection and is used widely in mobile phone apps, web apps, robotics, and for scientific research.

OpenCV is based on the the Viola-Jones algorithm. This video shows the process used by the Viola Jones algorithm, a cascading set of features that scans across an image at increasing sizes. By understanding how the algorithm detects a face, the process of designing an "anti-face" becomes more intuitive.

Click image for video: http://vimeo.com/12774628